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Artificial Intelligence

This column keeps returning to the healthcare industry because it is so much more complex and varied than so many others. Artificial intelligence (AI) coverage has focused on radiology, has moved to the operating theater, and has been discussed in the back office. Insurance and pharma fraud are arenas where AI risk analysis is useful. Now, along comes another area that is amenable to AI solutions. Its something many people think of as secondary, but is really a critical part of healthcare: physical therapy.

As someone who, many years ago, had an intriguing car crash, and who, not as many years ago, also proved he wasnt as young as he thought he was, by blowing out a knee, Im someone who is very aware of the need for physical therapy (PT). The basics of PT seem very simple: design therapies that cause repeated motions of damaged body parts, analyze that motion, then provide feedback to the patient and the medical community in order to help both improve. Its the capture and analysis of impact (yes, pun intended) of that motion which can prove complex.

Human physical therapists can see a lot of movement, but its impossible for them to capture all the necessary information. SWORD Health is a company focused on this unique healthcare segment. As they are a young company, they are focusing on a few key therapy areas. The hip, knee, lower back, shoulder, wrist and neck comprise more than 90 percent of all musculoskeletal issues in the U.S., said Virgilio Bento, CEO, SWORD Health. Rehabilitating them remotely requires a technology that can learn and expand.

One intriguing area that supports a separate call out section is the oft problematic issue of bias in testing. We know that visual neural networks have had problems identifying women of color. We know that, outside of AI, many drug trials dont include children, pregnant women, and other demographics who will need those drugs. Physical therapy is a healthcare sector that can avoid those problems.

There is already a body of PT information on the wide variety of demographics who receive PT. The ability to track far more information and to analyze it with demographic information (even anonymized for privacy), means that treatments can start with far more segmentation based on available information and then been quickly tuned on an individual basis based on direct, specific results. Starting with patterns based on more detailed segmentation and then transforming treatment on a case-by-case basis removes the bias issues that may be inherent in other areas of medicine or even in the minds of some medical personnel.

As has been regularly mentioned, AI is a tool, not a solution. The company isnt only working with machine learning. They make sensors to capture the information, with the kinematics being sent to the system via wireless communications. Then multiple techniques can be used to address the data. A mixture of deep learning and statistical linear regression is used to understand the progress of the therapies. Changing the therapy can then also be semi-automated, with the system suggesting changes. That doesnt need deep learning, as choosing the therapies is a rules based process.

As with all areas of healthcare dealing with patients, in the United State the FDA requires clearance of both new and updated appliances. The difference between hardware and AI is readily apparent with how each part is handled on change. When a hardware component is changed, detailed specifications can be sent to the FDA for fairly quick analysis and approval. The regulatory agency is still early in its analysis on how to manage AI, especially neural networks, so the process can be slower than with hardware.

AI is still a grey area, primarily through the fault of AI companies. While they like to talk about the black box that is a neural network, for instance, they know their layers, they know the nodes, the code and the weightings. While some of the inference is still not easily explicable, there is far more companies could provide to regulatory agencies if it were mandated.

In the lack of such transparency, expect for at least near-term job security for humans. They must remain in the loop, both as oversight for the AI and as a legal cover to say the AI is not making a prognosis but is providing the humans with options.

Deep learning and other machine learning techniques have an important place in healthcare, but it must be incorporated into the full patient treatment process, along with other technology. Unlike a deep learning system cranking along on its own in a research facility, investigating potential new drugs, AI must play well with other technology and processes the closer to patients it resides. Physical therapy is an excellent aspect of the needed growth, as it is a regular and visible part of patient treatment that includes humans, hardware and software interacting within a regulatory framework to improve patient outcomes.

See the article here:
The Breadth Of Healthcare Applications Of Artificial Intelligence Even Includes Physical Therapy - Forbes